Identification of adulterants in commercial samples of methyl eugenol is necessary because it is a botanical insecticide, a tephritid male attractant lure that is used to attract and kill invasive pests such as oriental fruit flies and melon flies on crops. In this study, Raman spectroscopy was used to qualitatively and quantitatively assess commercial methyl eugenol along with adulterants. For this purpose, commercial methyl eugenol was adulterated with different concentrations of xylene. The Raman spectral features of methyl eugenol and xylene in liquid formulations were examined, and Raman peaks were identified as associated with the methyl eugenol and adulterant. Principal component analysis (PCA) and partial least-squares regression analysis (PLSR) have been used to qualitatively and quantitatively analyze the Raman spectral features. PCA was applied to differentiate Raman spectral data for various concentrations of methyl eugenol and xylene. Additionally, PLSR has been used to develop a predictive model to observe a quantitative relationship between various concentrations of adulterated methyl eugenol and their Raman spectral data sets. The root-mean-square errors of calibration and prediction were calculated using this model, and the results were found to be 1.90 and 3.86, respectively. The goodness of fit of the PLSR model is found to be 0.99. The proposed approach showed excellent potential for the rapid, quantitative detection of adulterants in methyl eugenol, and it may be applied to the analysis of a range of pesticide products.
Raman spectroscopy has been used to characterize and quantify the solid dosage forms of the commercially available drug febuxostat. For this purpose, different formulations consisting of the febuxostat (API) and excipients with different concentrations of the API are prepared and analyzed by Raman spectroscopy to identify different spectral features related to the febuxostat API and excipients. Multivariate data analysis tools such as principal component analysis (PCA) and partial least-squares regression (PLSR) analysis are used for qualitative and quantitative analyses. PCA has been found to be useful for the qualitative monitoring of various solid dosage forms. PLSR analysis has led to the successful prediction of API concentration in the unknown samples with a sensitivity and a selectivity of 98 and 99%, respectively. Moreover, the root-mean-square error (RMSE) of calibration and validation of the PLSR model has been found to be 2.9033 and 1.35, respectively. Notably, it is found to be very helpful for the comparison between the self-made formulations of febuxostat and commercially available febuxostat tablets (40 and 80 mg) of two different brands (Gouric and Zurig). These results showed that Raman spectroscopy can be a useful and reliable technique for identifying and quantifying the active pharmaceutical ingredient (API) in commercially available solid dosage forms.
BACKGROUND: Raman spectroscopy is able to analyze non-invasively, disease related to body fluids. OBJECTIVES: For the qualitative and quantitative analysis of HCV serum samples surface-enhanced Raman spectroscopy (SERS) based method is developed. METHOD: Surface-enhanced Raman spectroscopy (SERS) technique is employed for analysis of filtrate portions of blood serum samples of hepatitis C virus (HCV) infected patients and healthy ones by using 50 kDa centrifugal filter device. The filtrate portions of the serum obtained in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire SERS spectral features of smaller proteins more effectively which are probably associated with Hepatitis C infection. Moreover, SERS spectral features of the filtrates of different level of viral load including low, medium and high viral loads are compared with SERS spectral features of the filtrate portions of healthy/control serum samples. SERS spectral data sets of different samples are further analyzed by using multivariate data analysis techniques such as principal component analysis (PCA) and partial least square regression (PLSR). Some SERS spectral features are solely observed in the filtrate portions of the serum samples of hepatitis C and their intensities are increased as the level of viral load increases and might be used for HCV diagnosis. RESULTS: PCA was found helpful for differentiation of SERS spectral data sets of filtrate portions of the serum samples of hepatitis C and healthy persons. The PLSR model helped for the quantification of viral loads in the unknown serum samples with 99% accuracy.
Hepatitis C , Photochemotherapy , Hepacivirus , Hepatitis C/diagnosis , Humans , Photochemotherapy/methods , Principal Component Analysis , Serum , Spectrum Analysis, Raman/methods
